AI-powered end-to-end e-commerce analytics platform integrating MySQL, advanced SQL modeling, executive BI dashboards (Power BI), and generative AI reporting to deliver insights on revenue growth, retention, operational efficiency, and customer lifetime value
Databricks SQL Medallion Architecture (Bronze-Silver-Gold) + Delta Lake powering a Power BI star schema -- surfaced $97.24K in e-commerce revenue leakage and a 26-day logistics outlier.
SQL-driven customer analytics dashboard built on the Olist Brazilian E-Commerce dataset. Answers four business questions — RFM segmentation, cohort retention, product affinity, and revenue leakage — using MySQL queries served via FastAPI and visualized in Next.js with Recharts.
End-to-end e-commerce sales analysis using MySQL on the Olist dataset to analyze revenue, orders, customers, monthly trends, repeat customers, and business KPIs.
End-to-end e-commerce analysis of 100k+ Olist orders using Excel, Power Query, SQL, and Tableau Public.
End-to-end e-commerce analysis using PostgreSQL & Excel to evaluate product yield, geographic logistics, and delivery delay impacts on CSAT.
Análise de e-commerce e logística do dataset Olist com tratamento em Python/SQL, prototipação no Figma e dashboard interativo no Power BI.
End-to-end data analysis investigating the impact of delivery delays on customer retention for Olist e-commerce using Excel, SQL Server, and Power BI.
Big data analytics on e-commerce datasets including Brazilian Olist marketplace data — data processing, insights, and exploratory analysis.
Customer segmentation project using RFM (Recency, Frequency, Monetary) analysis on the Brazilian E-Commerce (Olist) dataset to identify high-value customer behaviors.
Operational and customer-experience intelligence over the Olist Brazilian E-Commerce dataset. DuckDB/PostgreSQL warehouse, data-quality contracts, NLP over reviews, FastAPI serving layer.
End-to-end e-commerce analytics pipeline using PostgreSQL, SQL quality tests, Python, Streamlit, and the Olist dataset.
Analyzes 100K+ e-commerce transactions to uncover key business challenges like low customer retention and delivery delays using Python, SQL, and MySQL
SQL business analytics on real Olist e-commerce data — finance, product, customer RFM segmentation, and order fulfillment insights using SSMS/SQL Server.
Predict delivery time in days for e-commerce orders using LightGBM. Reduces MAE by 66% vs. rule-based estimates. Flask API deployed.
End-to-end data science project on ~100k Brazilian e-commerce orders — predicting churn risk customers with XGBoost, an interactive Dash BI dashboard, and a GPT-4o-mini powered customer recovery email agent.
A production-grade medallion lakehouse for e-commerce analytics and real-time order intelligence.
End-to-end analytics of Olist dataset showing insights on customers, sellers, and deliveries.
Automated ETL pipeline for Olist e-commerce data analysis using PostgreSQL, Python, and SQL.
Predicts the probability of late delivery for Brazilian e-commerce orders using XGBoost, built on the Olist dataset. Includes full EDA, a leakage-safe ML pipeline (SMOTE, feature selection, cross-validation), and a deployed Streamlit app for real-time risk scoring.
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